Design and Performance Evaluation of Image Processing Algorithms on GPUs

• Published in: IEEE transactions on parallel and distributed systems Vol. 22; no. 1; pp. 91 - 104
• Main Authors: In Kyu Park, Singhal, Nitin, Man Hee Lee, Sungdae Cho, Kim, Chris W
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Computer architecture; Computer vision; Concurrent computing; CUDA; Design engineering; Document management; GPGPU; GPU; Graphics processing unit; Image processing; Mathematical models; parallel implementation; Parallel processing; Parallel programming; Performance evaluation; Programming; Rendering; Scattering; Studies
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2010.115

In this paper, we construe key factors in design and evaluation of image processing algorithms on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. A set of metrics, customized for image processing, is proposed to quantitatively evaluate algorithm characteristics. In addition, we show that a range of image processing algorithms map readily to CUDA using multiview stereo matching, linear feature extraction, JPEG2000 image encoding, and nonphotorealistic rendering (NPR) as our example applications. The algorithms are carefully selected from major domains of image processing, so they inherently contain a variety of subalgorithms with diverse characteristics when implemented on the GPU. Performance is evaluated in terms of execution time and is compared to the fastest host-only version implemented using OpenMP. It is shown that the observed speedup varies extensively depending on the characteristics of each algorithm. Intensive analysis is conducted to show the appropriateness of the proposed metrics in predicting the effectiveness of an application for parallel implementation.